Dynamic Network Analysis - PhD level # 08-801

Offered Spring 2018

**The course Dynamic Network Analysis can be counted as an elective for security students in Information Security Policy & Management (MSISPM).

Course Description:

Who knows who? Who knows what? Who is influential? What is the social
network, the knowledge network, the activity network? How do ideas,
products & diseases propagate through groups and impact these networks?
Does social media change the way these networks operate?
Questions such as these & millions of others require a network perspective and an understanding
of how ties among people, ideas, things, & locations connect, constrain
& enable activity. In the past decade there has been an explosion of
interest in network science moving from the work on social networks and
graph theory to statistical and computer simulation models. Network
analysis, like statistics, now plays an role in most empirical fields.

This course provides insight into this broad and growing field from a
cross-disciplinary perspective. Fundamental metrics and advanced
methods are covered, with attention to the application areas where these
can and have been used. In class projects will cover the application
and development of techniques for analyzing a range of networks
including, but not limited to, social networks, social-media networks
(e.g. twitter networks), geo-spatial constraints on networks, dynamic
networks, semantic networks, and alliance networks. Methods for network
data collection, analysis, visualization, and interpretation are
covered. Students produce original research in which network data is
analyzed using the methods covered in the class.